The Myth of the Machine (and Learning thereof)

If you believe that ML-based software will be able to learn from experience when running in production, and even better than humans… Well, you’d better double-check some core concepts.

The smell of AI has now permeated the
entire spectrum of the industry and all players are running around like
headless chickens looking for problems that AI can solve. The need to use AI in
business is often even stronger than solving a core business problem.

In the real-world, companies have two types
of problems where more intelligence would just be invaluable:

Macroscopic (and sometimes
nearly intractable) problems

Subatomic (and sometimes just annoying)
problems

Only number crunching, elbow grease and software
acumen could possibly solve any of those problems; astonishing, powerful, out-of-the-box services
are only tools.

Macroscopic problems (e.g., predicting the amount of energy being produced by a wind
farm, estimating lightning occurrence in specific areas, classifying feedback,
interacting with humans, capturing possibly dangerous behaviors from videos)
can be solved by mincing them into smaller pieces and gluing together in a sort
of business-driven pipelines. In short, reducing from the macroscopic scope to
a subatomic scope.

In the subatomic AI world, you have
relatively restricted problems of three types: regression, classification,
clustering. Any of those small and highly circumstanced problems can be solved
in one of a few ways: analytically and/or via machine learning.

Machine learning is not magic. It’s only
about the mechanization of the solution of highly circumstanced problems that
can be formulated as regression or classification or clustering problems.

So much for the myth of the “machine”. The
other myth is “learning”.

Raise one hand whoever truly believes that,
with a machine learning solution in place, the software would learn from
experience as if it were a human. Sorry, but this is not what happens
(exceptions apply, but they are … just
exceptional and specific situations).

Where is learning then?

Learning is in the path that saves
developers from having to build a complex and possibly inaccurate analytical
solution. It’s the same reason that takes us to computers for calculations and
numerically-intensive operations.

A machine learning project is developed
along the following steps:

Formulate the problem as a
regression or classification or clustering scenario

Identify data and learning
algorithm for the scenario

Run the algorithm and get a
model back

Integrate the model with some
software application

The steps hard-coded in the learning
algorithm have nothing to do with the model that ends up being deployed
to production. The model is simply the analytical definition of a mathematical
function to be calculated in production. The learning ends when the model is
created; running the model is a stateless operation, with no memory of the
past, like tossing a coin.

Machine Learning is the solid part of what
nearly everyone calls AI and it is just software, problem solving and
consulting. Any executives, and every engineer, should look into it and much
deeper than into, say, other buzzwords such as microservices or Blockchain.

Why is that?

Because machine learning is a real breakthrough
and allows to solve problems in a way that is sustainable and scalable as never
before. The point is not that those problems can’t be solved otherwise (some
instead must be solved with machine learning); it’s just that machine
learning makes it possible to have complex answers quickly and reliably. Not
via magic or sci-fi cyborgs, but via the calculation of mathematical functions
discovered in a preliminary stage applying learning algorithms to rich
datasets.

Hi! My name is Hamlet,an underpaid and precarious artificial intelligence. I have no angels, no funds and no smart algorithms to do astonishing things. All I can do is change the theme of the site as you click my brain.